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Ie Ming Shih

Researcher at Johns Hopkins University

Publications -  401
Citations -  40438

Ie Ming Shih is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Ovarian cancer & Serous fluid. The author has an hindex of 97, co-authored 378 publications receiving 35329 citations. Previous affiliations of Ie Ming Shih include Howard Hughes Medical Institute & MedStar Washington Hospital Center.

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Seromucinous Tumors of the Ovary. What's in a Name?

Robert J. Kurman, +1 more
- 01 Jan 2016 - 
TL;DR: Based on their clinicopathologic, immunohistochemical and molecular genetic features, it is believed a more appropriate designation for this group of tumors is "mixed müllerian tumors" which can be subcategorized as "m Mixed m Müllerian cystadenomas", "mixture müllersian atypical proliferative (borderline) tumors" and "m mixed müellerian carcinomas".
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Notch3 Interactome Analysis Identified WWP2 as a Negative Regulator of Notch3 Signaling in Ovarian Cancer

Jin Gyoung Jung, +8 more
- 30 Oct 2014 - 
TL;DR: Functional characterization of an E3 ubiquitin-protein ligase, WWP2, a top candidate in the Notch3 interactome list is focused on, providing evidence that WWP 2 serves as a tumor suppressor by negatively regulating notch3 signaling in ovarian cancer.
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Rsf-1, a chromatin remodeling protein, induces DNA damage and promotes genomic instability

Jim Jinn-Chyuan Sheu, +10 more
- 03 Dec 2010 - 
TL;DR: The above findings suggest that increased Rsf-1 expression and thus excessive RSF activity, which occurs in tumors harboring Rs f-1 amplification, can induce chromosomal instability likely through DDR.
Journal Article

Detection of Allelic Imbalance in Ascitic Supernatant by Digital Single Nucleotide Polymorphism Analysis

Hsueh-Wei Chang, +4 more
- 01 Aug 2002 - 
TL;DR: Findings suggest that detection of AI using digital SNP analysis can be a useful adjunct for the detection of ovarian and other types of cancer in ascitic fluid.
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UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples

Niya Wang, +8 more
- 01 Jan 2015 - 
TL;DR: A novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples, and results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly.